Therapeutic potential of vagal neurostimulation to reduce food intake

肥胖影响了近40%的美国成年人，并与高水平的共病有关，包括 癌症、心血管疾病和糖尿病。虽然副作用最小的有效治疗方法是 缺乏迷走神经刺激(VNS)可以减轻体重，抑制摄食行为。几乎没有什么 然而，对其机制的洞察，以及VNS是否影响摄食和体重结果尚不清楚 来自非特定的副作用，如恶心。当前的应用程序通过以下方式直接解决这些问题 评估胃肠(GI)肌电变化作为VNS影响进食的潜在机制 行为，同时将这些反应与呕吐激活进行比较。我们计划用雪貂来完成这项任务 模型，这是研究呕吐、迷走神经和胃肠道生理学的黄金标准。我们将测试 假设电刺激迷走神经可以减少食物摄入量而不会触发 恶心的症状，如胃肠肌电反应中断、呕吐和呕吐。我们会 完成三个目标。目的1：确定摄食行为中的个体化胃肠道肌电模式 机器学习分类。动物将被植入附着在GI浆膜上的平面电极 胃底近端至十二指肠远端。我们将使用机器学习对GI进行分类 进食与呕吐相关状态的肌电模式比较，包括由 已知胃内注射依米汀和高幅度高频率的迷走神经刺激可引发呕吐。目标2：测试疗效 腹部VNS在减少进食时不会引发胃肠道肌电反应中断、呕吐、 和呕吐物。将使用各种刺激参数评估动物腹部VNS的影响 摄食行为和多部位胃肠肌电记录。目的3：确定颈椎迷走神经根治术的疗效 控制进食大小而不产生偏离目标的影响(胃肠道肌电反应中断、呕吐、 呕吐、心率或血压的变化)。我们将测试颈椎VNS参数对 进食行为、胃肠肌电反应、干呕、呕吐、心率变异性和血压。我们的 方法是创新的，因为我们将使用机器学习分类来检测个性化的GI 清醒自由活动动物的肌电反应模式用于比较治疗和非靶点效应 VNS对进食、胃肠活动、呕吐和心血管功能的影响。这项计划中的研究具有重要意义 因为VNS疗法可能为高肥胖症患者提供一线治疗选择 行为或药物治疗难以奏效，这与其他减肥手术不同， 例如胃旁路，通过改变刺激参数、切换 设备关闭或完全删除。

Obesity affects almost 40% percent of US adults and is associated with high levels of comorbidities, including cancer, cardiovascular disease, and diabetes. Although effective treatments with minimal side effects are lacking, vagus nerve stimulation (VNS) can reduce body weight and suppress feeding behavior. There is little insight, however, into its mechanism and it is unclear whether VNS effects on feeding and body weight result from non-specific side effects, such as nausea. The current application directly addresses these issues by assessing gastrointestinal (GI) myoelectric changes as a potential mechanism for effects of VNS on feeding behavior, while comparing these responses to emetic activation. We plan to accomplish this by using a ferret model, which is a gold-standard for studying emesis, vagus nerve, and GI physiology. We will test the hypothesis that electrical stimulation of the vagus nerve can reduce food intake without triggering indicators of nausea, such as disrupted GI myoelectric responses, retching, and vomiting. We will complete three Aims. Aim 1: Define the individualized GI myoelectric patterns during feeding behavior using machine learning classification. Animals will be implanted with planar electrodes attached to the GI serosal surface from proximal gastric fundus to distal duodenum. We will use machine learning to classify GI myoelectric patterns of meal consumption compared to emetic-related states, including those elicited by intragastric emetine and high amplitude and frequency VNS known to trigger emesis. Aim 2: Test the efficacy of abdominal VNS on reducing meal size without triggering disruptions of GI myoelectric responses, retching, and emesis. Animals will be assessed for effects of abdominal VNS using a variety of stimulus parameters on feeding behavior and multi-site GI myoelectric recordings. Aim 3: Determine the efficacy of cervical VNS in controlling meal size without producing off-target effects (disruptions of GI myoelectric responses, retching, emesis, changes in heart rate, or blood pressure). We will test the impact of cervical VNS parameters on feeding behavior, GI myoelectric responses, retching, emesis, hear rate variability, and blood pressure. Our approach is innovative because we will use machine learning classification to detect individualized GI myoelectric response patterns in an awake free-moving animal for comparing therapeutic and off-target effects of VNS on feeding, GI activity, emesis, and cardiovascular function. This planned research is significant because VNS therapy can potentially provide a frontline treatment option for patients with high levels of obesity refractory to behavioral or pharmacological therapy, which unlike other surgical interventions for weight loss, such as gastric bypass, is potentially tunable and reversible by changing stimulation parameters, switching the device off, or complete removal.